class_docstr: Class with all code to run experiment.
class_docstr_tok: ["Class","with","all","code","to","run","experiment","."]
docstr: Computes learning rate for given fractional epoch.
docstr_tok: ["Computes","learning","rate","for","given","fractional","epoch","."]
code: def learning_rate(self, epoch: float):
      """Computes learning rate for given fractional epoch."""
      # Linear warm up to base_learning_rate value for first 5 epochs.
      # Then use 1.0 * base_learning_rate until epoch 30
      # Then use 0.1 * base_learning_rate until epoch 60
      # Then use 0.01 * base_learning_rate until epoch 80
      # Then use 0.001 * base_learning_rate until until end of traning.
      lr_linear_till = 5
      boundaries = jn.array((30, 60, 80))
      values = (
          jn.array([1.0, 0.1, 0.01, 0.001]) * self.base_learning_rate
      )
  
      index = jn.sum(boundaries < epoch)
      lr = jn.take(values, index)
      return lr * jn.minimum(1.0, epoch / lr_linear_till)
  
file_path: objax/examples/image_classification/imagenet_resnet50_train.py
imports: import os
  import time
  import jax
  import jax.numpy
  import numpy
  from absl import app, flags, logging
  import objax
  from examples.image_classification import imagenet_resnet50_data
  from objax.zoo.resnet_v2 import ResNet50
label_desc: Use image node_id 5 for calling the Experiment obj's underlying member method code with example usage: obj.learning_rate(epoch) and returns: unknown